Patch Matching

33 papers with code • 2 benchmarks • 4 datasets

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Most implemented papers

Working hard to know your neighbor's margins: Local descriptor learning loss

DagnyT/hardnet NeurIPS 2017

We introduce a novel loss for learning local feature descriptors which is inspired by the Lowe's matching criterion for SIFT.

Rotation equivariant vector field networks

di-marcos/RotEqNet ICCV 2017

In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.

Distinctive Image Features from Scale-Invariant Keypoints

kornia/kornia 5 Jan 2004

This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene.

MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching

hanxf/matchnet CVPR 2015

We perform a comprehensive set of experiments on standard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods.

Continuous 3D Label Stereo Matching using Local Expansion Moves

t-taniai/LocalExpStereo 28 Mar 2016

The local expansion moves extend traditional expansion moves by two ways: localization and spatial propagation.

Attention Concatenation Volume for Accurate and Efficient Stereo Matching

gangweix/acvnet CVPR 2022

Stereo matching is a fundamental building block for many vision and robotics applications.

Person Re-identification with Correspondence Structure Learning

YangShenSJTUs/ReIDCorresStructure ICCV 2015

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification.

Deep Colorization

djflstkddk/Auto-Colorization ICCV 2015

This paper investigates into the colorization problem which converts a grayscale image to a colorful version.

Person Re-Identification via Recurrent Feature Aggregation

daodaofr/caffe-re-id 23 Jan 2017

We address the person re-identification problem by effectively exploiting a globally discriminative feature representation from a sequence of tracked human regions/patches.

L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space

yuruntian/L2-Net CVPR 2017

In this paper, we propose to learn high per- formance descriptor in Euclidean space via the Convolu- tional Neural Network (CNN).